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Kingdee MCP Server

Kingdee MCP Server

by WaHaiLong
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

kingdee_query_cost_trend

Read-onlyIdempotent

Query cost price trends for materials over time, with filters for material number and date range. Returns a JSON list showing cost price changes.

Instructions

查询成本价趋势分析表（STK_CostTrend）。

分析物料成本价格的变化趋势。

常用 filter_string：

  • 指定物料: "FMaterialId.FNumber='MAT001'"

  • 指定日期范围: "FDate>='2024-01-01' and FDate<='2024-12-31'"

推荐 field_keys： FMaterialId.FNumber,FMaterialId.FName,FDate,FCostPrice,FPriceChange

Returns: str: JSON 格式的成本价趋势列表

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "kingdee_query_cost_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the bar is lower. The description adds that it returns a JSON list of cost trends, but does not disclose other behavioral traits like rate limits, authentication needs, or edge cases. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose, followed by concise examples and a note on return format. Every sentence adds value with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the read-only nature, annotations, and existence of an output schema, the description is largely complete. It could mention prerequisites like needing material IDs, but overall provides sufficient context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides specific examples of filter_string (material and date range) and recommended field_keys, adding meaningful context beyond the generic schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries the cost price trend analysis table (STK_CostTrend) and analyzes material cost price trends. It distinguishes from siblings by specifying the exact table and purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives examples of filter_string and field_keys, implying usage context, but does not explicitly state when to use this tool over other cost-related query tools (e.g., kingdee_query_material_cost) or provide exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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